Network analysis of psoriasis reveals biological pathways and roles for coding and long non-coding RNAs.

Richard Ahn, Rashmi Gupta, Kevin Lai, Nitin Chopra, Sarah T Arron, Wilson Liao
Author Information
  1. Richard Ahn: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA. richard.ahn@ucsf.edu. ORCID
  2. Rashmi Gupta: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
  3. Kevin Lai: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
  4. Nitin Chopra: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
  5. Sarah T Arron: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.
  6. Wilson Liao: Department of Dermatology, University of California, San Francisco, 2340 Sutter Street, Box 0808, San Francisco, CA, 94143-0808, USA.

Abstract

BACKGROUND: Psoriasis is an immune-mediated, inflammatory disorder of the skin characterized by chronic inflammation and hyperproliferation of the epidermis. Differential expression analysis of microarray or RNA-seq data have shown that thousands of coding and non-coding genes are differentially expressed between psoriatic and healthy control skin. However, differential expression analysis may fail to detect perturbations in gene coexpression networks. Sensitive detection of such networks may provide additional insight into important disease-associated pathways. In this study, we applied weighted gene coexpression network analysis (WGCNA) on RNA-seq data from psoriasis patients and healthy controls.
RESULTS: RNA-seq was performed on skin samples from 18 psoriasis patients (pre-treatment and post-treatment with the TNF-α inhibitor adalimumab) and 16 healthy controls, generating an average of 52.3 million 100-bp paired-end reads per sample. Using WGCNA, we identified 3 network modules that were significantly correlated with psoriasis and 6 network modules significantly correlated with biologic treatment, with only 16 % of the psoriasis-associated and 5 % of the treatment-associated coexpressed genes being identified by differential expression analysis. In a majority of these correlated modules, more than 50 % of coexpressed genes were long non-coding RNAs (lncRNA). Enrichment analysis of these correlated modules revealed that short-chain fatty acid metabolism and olfactory signaling are amongst the top pathways enriched for in modules associated with psoriasis, while regulation of leukocyte mediated cytotoxicity and regulation of cell killing are amongst the top pathways enriched for in modules associated with biologic treatment. A putative autoantigen, LL37, was coexpressed in the module most correlated with psoriasis.
CONCLUSIONS: This study has identified several networks of coding and non-coding genes associated with psoriasis and biologic drug treatment, including networks enriched for short-chain fatty acid metabolism and olfactory receptor activity, pathways that were not previously identified through differential expression analysis and may be dysregulated in psoriatic skin. As these networks are comprised mostly of non-coding genes, it is likely that non-coding genes play critical roles in the regulation of pathways involved in the pathogenesis of psoriasis.

Keywords

References

  1. J Invest Dermatol. 2004 Nov;123(5):880-7 [PMID: 15482475]
  2. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 [PMID: 16199517]
  3. Reprod Sci. 2014 Feb 14;21(7):892-897 [PMID: 24532216]
  4. J Invest Dermatol. 2012 Jan;132(1):246-9 [PMID: 21850022]
  5. Nature. 2013 Dec 19;504(7480):451-5 [PMID: 24226773]
  6. Genome Med. 2015 May 13;7(1):39 [PMID: 25991924]
  7. Genome Biol. 2013 Apr 25;14(4):R36 [PMID: 23618408]
  8. Oncogenesis. 2014 Dec 22;3:e135 [PMID: 25531430]
  9. Nat Commun. 2014 Dec 03;5:5621 [PMID: 25470744]
  10. Nat Biotechnol. 2013 Jan;31(1):46-53 [PMID: 23222703]
  11. Exp Dermatol. 2013 Apr;22(4):255-61 [PMID: 23528210]
  12. Nature. 2012 Jun 28;486(7404):549-53 [PMID: 22722857]
  13. Cell Host Microbe. 2010 Sep 16;8(3):236-47 [PMID: 20833375]
  14. PLoS Genet. 2012 Jan;8(1):e1002480 [PMID: 22291609]
  15. Proc Natl Acad Sci U S A. 2006 Nov 14;103(46):17402-7 [PMID: 17090670]
  16. Immunity. 2015 Oct 20;43(4):817-29 [PMID: 26488817]
  17. J Invest Dermatol. 2010 Jul;130(7):1829-40 [PMID: 20220767]
  18. Bioinformatics. 2005 Mar;21(6):754-64 [PMID: 15479708]
  19. Hum Mol Genet. 2001 Aug 15;10(17):1793-805 [PMID: 11532989]
  20. Arterioscler Thromb Vasc Biol. 2013 Jun;33(6):1427-34 [PMID: 23539213]
  21. Nat Genet. 2015 Mar;47(3):199-208 [PMID: 25599403]
  22. BMC Bioinformatics. 2008 Dec 29;9:559 [PMID: 19114008]
  23. PLoS Genet. 2013 Jun;9(6):e1003569 [PMID: 23818866]
  24. Arthritis Res Ther. 2015 Feb 11;17:29 [PMID: 25890351]
  25. Biochim Biophys Acta. 2013 Dec;1833(12):3218-27 [PMID: 24080087]
  26. J Invest Dermatol. 2010 May;130(5):1213-26 [PMID: 19812592]
  27. Nat Rev Genet. 2014 Jan;15(1):7-21 [PMID: 24296535]
  28. Physiol Genomics. 2014 Aug 1;46(15):533-46 [PMID: 24844236]
  29. Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63 [PMID: 24259432]
  30. J Invest Dermatol. 2014 Nov;134(11):2823-32 [PMID: 24999593]
  31. J Invest Dermatol. 2009 Dec;129(12):2795-804 [PMID: 19571819]
  32. BMC Syst Biol. 2008 Nov 06;2:95 [PMID: 18986552]
  33. J Invest Dermatol. 2014 Jul;134(7):1828-38 [PMID: 24441097]
  34. Science. 2003 Oct 10;302(5643):249-55 [PMID: 12934013]
  35. J Invest Dermatol. 2003 Jul;121(1):132-41 [PMID: 12839573]
  36. J Clin Invest. 2014 Mar;124(3):1027-36 [PMID: 24509084]
  37. Physiol Genomics. 2003 Mar 18;13(1):69-78 [PMID: 12644634]
  38. J Immunol. 2015 Jun 1;194(11):5375-87 [PMID: 25895533]
  39. BMC Bioinformatics. 2006 Aug 16;7:381 [PMID: 16914048]
  40. BMC Genomics. 2013 Nov 23;14:825 [PMID: 24267790]
  41. J Invest Dermatol. 2016 Mar;136(3):603-9 [PMID: 27015450]
  42. Arthritis Rheum. 2009 Jan;60(1):81-92 [PMID: 19116902]

Grants

  1. R01 AR065174/NIAMS NIH HHS
  2. T32 AR007175/NIAMS NIH HHS
  3. U01 AI119125/NIAID NIH HHS

MeSH Term

Adult
Case-Control Studies
Female
Gene Expression Profiling
Gene Expression Regulation
Gene Regulatory Networks
Humans
Male
Middle Aged
Psoriasis
RNA, Long Noncoding
RNA, Messenger
Signal Transduction
Skin
Transcriptome

Chemicals

RNA, Long Noncoding
RNA, Messenger

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